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US 12,683,031 B1Granted
Systems and methods for training a model to predict survival time for a patient
AssigneePathAI, Inc.
InventorsAndrew H. Beck, Aditya Khosla
Life of this patent
Estimating the term…
Abstract
In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.
Claims20
A method, comprising:
- determining whether a trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from an experimental treatment compared to a standard treatment or a control treatment, wherein the determining comprises:
- processing, using the trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated prostate images associated with a first group of prostate cancer patients, to predict survival data for prostate cancer patients in the first group of prostate cancer patients, wherein the first group of prostate cancer patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated prostate images associated with a second group of prostate cancer patients, to predict survival data for prostate cancer patients in the second group, wherein the second group of prostate cancer patients belongs to a control treatment group of the randomized controlled clinical trial;
- and
- determining whether the trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from the experimental treatment compared to the standard treatment or the control treatment based on the predicted survival data for the prostate cancer patients in the first group and the predicted survival data for the prostate cancer patients in the second group.
2 more elements
A system, comprising:
- at least one computer hardware processor;
- and
- at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
- determining whether a trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from an experimental treatment compared to a standard treatment or a control treatment, wherein the determining comprises:
- processing, using the trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated prostate images associated with a first group of prostate cancer patients, to predict survival data for prostate cancer patients in the first group of prostate cancer patients, wherein the first group of prostate cancer patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated prostate images associated with a second group of prostate cancer patients, to predict survival data for prostate cancer patients in the second group, wherein the second group of prostate cancer patients belongs to a control treatment group of the randomized controlled clinical trial;
- and
- determining whether the trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from the experimental treatment compared to the standard treatment or the control treatment based on the predicted survival data for the prostate cancer patients in the first group and the predicted survival data for the prostate cancer patients in the second group.
5 more elements
A non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform:
- determining whether a trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from an experimental treatment compared to a standard treatment or a control treatment, wherein the determining comprises:
- processing, using the trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated prostate images associated with a first group of prostate cancer patients, to predict survival data for prostate cancer patients in the first group of prostate cancer patients, wherein the first group of prostate cancer patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated prostate images associated with a second group of prostate cancer patients, to predict survival data for prostate cancer patients in the second group, wherein the second group of prostate cancer patients belongs to a control treatment group of the randomized controlled clinical trial;
- and
- determining whether the trained model accurately identifies a subset of prostate cancer patients that is likely to benefit from the experimental treatment compared to the standard treatment or the control treatment based on the predicted survival data for the prostate cancer patients in the first group and the predicted survival data for the prostate cancer patients in the second group.
2 more elements
Dependencies are inferred from the claim text.